Tinybird is a data platform for data and engineering teams to solve complex real-time, operational, and user-facing analytics use cases at any scale. Tinybird makes it easy to import data from a variety of sources, use SQL to filter, aggregate, and join that data, and publish low-latency, high-concurrency RESTful API endpoints.
We assessed the ability of popular LLMs to generate accurate and efficient SQL from natural language prompts. Using a 200 million record dataset from the GH Archive uploaded to Tinybird, we asked the LLMs to generate SQL based on 50 prompts.
Continuous profiling for analysis of CPU and memory usage, down to the line number and throughout time. Saving infrastructure cost, improving performance, and increasing reliability.
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